LLMs ·

DeepSeek V4 Pro (April 2026)

Parameters
1,599B (49.0B active)
Architecture
Mixture of experts
Max context
1,024K tokens
License
mit
Released
2026-04

How to run it

Original weights · Hugging Face

Full-precision files from DeepSeek, for fine-tuning, vLLM, Transformers.

huggingface.co/deepseek-ai/DeepSeek-V4-Pro (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

Apps that run DeepSeek V4 Pro (April 2026)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

Runs the MLX build, typically up to 10% faster than GGUF for a model with 49B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, AMD Radeon

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux server, 80 GB+ GPUs

Docker Model Runner

Handy if you already work in Docker.

Also good:

App Type Opens it as Systems Engine Start
LM Studio Desktop app MLX macOSWindowsLinux llama.cpp, MLX Open in LM Studio
Jan Desktop app MLX macOSWindowsLinux llama.cpp, MLX Get Jan
MLX LM CLI + API MLX macOS MLX 
mlx_lm.chat --model mlx-community/DeepSeek-V4-Pro-4bit
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve deepseek-ai/DeepSeek-V4-Pro
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V4-Pro
Docker Model Runner CLI + API Original weights macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/deepseek-ai/DeepSeek-V4-Pro
Unsloth Desktop Desktop app MLX macOSWindowsLinux llama.cpp, MLX, stable-diffusion.cpp Get Unsloth Desktop
Atomic Chat Desktop app MLX macOSWindowsLinux llama.cpp, MLX-VLM Open in Atomic Chat
Pi Coding agent MLX macOSWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
Hermes Agent Assistant MLX macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenClaw Assistant MLX macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenCode Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Codex CLI Coding agent MLX macOSWSLWindowsLinux uses Ollama or any OpenAI-compatible server Point it at a llama.cpp or MLX LM server
OpenHands Coding agent MLX macOSWSLWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Zed Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Cline Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open Interpreter Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Goose Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Aider Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Crush Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Qwen Code Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Kilo Code Coding agent MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open WebUI Assistant MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Khoj Assistant MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Letta Assistant MLX macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Text Generation WebUI Desktop app Original weights macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI
Msty Studio Desktop app MLX macOSWindowsLinux Ollama, llama.cpp, MLX Get Msty Studio
Inferencer Desktop app MLX macOS MLX Get Inferencer
exo Server MLX macOSLinux MLX Get exo
Continue Coding agent MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
SillyTavern Assistant MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
LibreChat Assistant MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Cherry Studio Assistant MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Chatbox Assistant MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PrivateGPT Assistant MLX macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

KV cache: compressed KV cache (single 512-value head per layer); estimate assumes 8-bit KV.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 640 GB 641 GB 642 GB All files (opens in a new tab)
Q3_K_M Low 783 GB 785 GB 786 GB All files (opens in a new tab)
Q4_K_M Good 975 GB 976 GB 978 GB All files (opens in a new tab)
Q5_K_M High 1,135 GB 1,136 GB 1,138 GB All files (opens in a new tab)
Q6_K High 1,311 GB 1,312 GB 1,314 GB All files (opens in a new tab)
Q8_0 Very high 1,695 GB 1,696 GB 1,697 GB All files (opens in a new tab)
F16 Full 3,198 GB 3,199 GB 3,200 GB Original weights (opens in a new tab)

Best hardware for DeepSeek V4 Pro (April 2026)

None. No single GPU or device in our catalog runs this model on its own; it needs several GPUs working together.

Check it on your own hardware

Run it on a cluster

Devices that can’t run DeepSeek V4 Pro (April 2026) alone can run it together. These example clusters do:

Try it with your own devices

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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